https://cis.temple.edu/~wu/teaching/Spring%202013/handoff.pdf
1.1 INTRODUCTION Mobility is the most important feature of a wireless cellular communication system. Usu-ally, continuous service is achieved by supporting handoff (or handover) from one cell to another. Handoff is the process of changing the channel (frequency, time slot, spreading code, or combination of them) associated with the current connection while a call is in progress. It is often ...
https://cis.temple.edu/~giorgio/cis307/readings/futex.pdf
This exemplifies that using futexes is really tricky since they provide problems even to their inventors. This docu-ment will hopefully provide correct and detailed instruc-tions on how to use futexes. First an understanding of the kernel interface and its semantic is needed.
https://sites.temple.edu/sserrano/files/2020/08/18-Analysis-of-Muskingum-Equation-Based-Flood-Routing-Schemes.pdf
By John J. Gelegenis1 and Sergio E. Serrano2 ABSTRACT: The linear Muskingum method continues to be a simple and popular procedure for river flood routing. An alternative algorithm for the numerical estimation of the Muskingum routing parameters is presented. Fully implicit and semi-implicit finite-difference schemes are compared for accuracy with respect to the tradi-tional graphical procedure ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/jiang_www_2020.pdf
ABSTRACT Serendipity recommendation has attracted more and more atten-tion in recent years; it is committed to providing recommendations which could not only cater to users’ demands but also broaden their horizons. However, existing approaches usually measure user-item relevance with a scalar instead of a vector, ignoring user preference direction, which increases the risk of unrelated ...
https://cis.temple.edu/~wu/research/publications/Publication_files/Joint%20Dynamic%20Grouping%20and%20Gradient%20Coding%20for%20Time-critical%20Distributed%20Machine%20Learning%20in%20Heterogeneous%20Edge%20Networks-FINAL-VERSION.pdf
Abstract—In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely stragglers. The parameter server even abandons stragglers which fail to return ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/1-s2.0-S0957417424018475-main.pdf
On this basis, we proposed MSS-GCN, a robust framework that gen-erates multi-granular and synchronized spatial–temporal features im-paired by previous factorized paradigms.